Embedded Online Optimization for Model Predictive Control at Megahertz Rates

J. L. Jerez, P. J. Goulart, S. Richter, G. Constantinides, E. C. Kerrigan and M. Morari

@article{JGRetal:2014,
  author = {J. L. Jerez and P. J. Goulart and S. Richter and G. Constantinides and E. C. Kerrigan and M. Morari},
  title = {Embedded Online Optimization for Model Predictive Control at Megahertz Rates},
  journal = {IEEE Transactions on Automatic Control},
  year = {2014},
  volume = {59},
  number = {12},
  pages = {3238-3251},
  url = {http://dx.doi.org/10.1109/TAC.2014.2351991},
  doi = {10.1109/TAC.2014.2351991}
}

Faster, cheaper, and more power efficient optimization solvers than those currently possible using general-purpose techniques are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.